---
title: "Data import and manipulation in poppr version `r packageVersion('poppr')`"
author: Zhian N. Kamvar and Niklaus J. Grünwald
date: "`r Sys.Date()`"
bibliography: the_bibliography.bib
output: 
  rmarkdown::html_vignette:
    fig_caption: yes
    fig_width: 5
    fig_height: 5
    toc: true
    toc_depth: 1
vignette: >
  %\VignetteIndexEntry{Data Import and Manipulation}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, echo = FALSE, message = FALSE, warning = FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE, tidy = FALSE)
knitr::opts_chunk$set(fig.align = "center", fig.show = 'asis')
knitr::opts_knit$set(eval.after = 'fig.cap')
library("knitcitations")
cite_options(citation_format = "pandoc", max.names = 3, style = "html", 
             hyperlink = "to.doc")
bib <- read.bibtex("the_bibliography.bib")
```

```{r poppr_funk, eval = TRUE, echo = FALSE}
print_command <- function(funk){
  fargs <- formals(funk)

  lapply(names(fargs), function(arg_name, fargs){
    arg <- fargs[[arg_name]]
    if (missing(arg)){
      fargs[[arg_name]] <<- as.symbol(arg_name)
      names(fargs)[names(fargs) == arg_name] <<- ""
    }
  }, fargs)
  fargs$call <- as.symbol(funk)
  fargs <- fargs[c(length(fargs), 1:(length(fargs) - 1))]
  return(as.call(fargs))
}
```


<center>

Abstract
========

*Poppr* provides open-source, cross-platform tools for quick analysis of
population genetic data enabling focus on data analysis and interpretation.
While there are a plethora of packages for population genetic analysis, few are
able to offer quick and easy analysis of populations with mixed reproductive
modes. *Poppr*'s main advantage is the ease of use and integration with other
packages such as *adegenet* and *vegan*, including support for novel methods
such as clone correction, multilocus genotype analysis, calculation of Bruvo's
distance, and the index of association. New features in version 2.0 include
generation of minimum spanning networks with reticulation, calculation of the
index of association for genomic data, and filtering multilocus genotypes based
on genetic distance.

<br>
<br>

<img src="jalapeno_logo.png" width="300">

<br>
<br>
</center>

Introduction {#intro}
============

Purpose {#intro:purpose}
-------

*Poppr* is an R package with convenient functions for analysis of genetic data
with mixed modes of reproduction including sexual and clonal reproduction. While
there are many R packages in CRAN and other repositories with tools for
population genetic analyses, few are appropriate for populations with mixed
modes of reproduction. There are several stand-alone programs that can handle
these types of data sets, but they are often platform specific and often only
accept specific data types. Furthermore, a typical analysis often involves
switching between many programs, and converting data to each specific format.

*Poppr* is designed to make analysis of populations with mixed reproductive
modes more streamlined and user friendly so that the researcher using it can
focus on data analysis and interpretation. *Poppr* allows analysis of haploid
and diploid dominant/co-dominant marker data including microsatellites, Single
Nucleotide Polymorphisms (SNP), and Amplified Fragment Length Polymorphisms
(AFLP). To avoid creating yet another file format that is specific to a program,
*poppr* was created on the backbone of the popular R package *adegenet* and can
take all the file formats that *adegenet* can take (Genpop, Genetix, Fstat, and
Structure) and newly introduces compatibility with GenAlEx formatted files
(exported to CSV). This means that anything you can analyze in *adegenet* can be
further analyzed with *poppr*.

The real power of *poppr* is in the data manipulation and analytic tools.
*Poppr* has the ability to bootstrap, clone-censor, and subset data sets. With
*poppr* you can also quickly calculate Bruvo's distance, the index of
association, and easily determine which multilocus genotypes are shared across
populations.

In version 2.0, tools for genomic data were introduced [@kamvar2015novel]. These
tools allow researchers to define what it means to be a clone lost in a sea of
genomic data, generate bootstrapped dendrograms with any genetic distance, and
calculate minimum spanning networks with reticulations to reveal the underlying
population structure of your clonal data.

Resources
---------

This vignette will cover all of the material you need to know to efficiently
analyze data in *poppr*. For information on methods of analysis (eg. index of
association, distance measures, AMOVA, ...), please read the manual pages 
provided for each function.

As *poppr* expanded from version 1.0, the vignette also expanded to be 80+
pages. As a result, it became clear that over 22,000 words was less of a manual
and more of a novella with a terrible plot. To remedy this, this vignette will
focus only on data manipulation and a separate vignette, "algo", has been
written to give algorithmic details of analyses introduced with *poppr*.

As of spring 2014, Drs. Niklaus J. Grünwald, Sydney E. Everhart, and I have co-
authored a primer on using R for population genetic analysis. It is located at
<http://grunwaldlab.github.io//Population_Genetics_in_R> and the source code can
be found [on our github site][githubsite].

Getting Help
------------

If you have any questions or feedback, feel free to send a message to
the *poppr* forum at <http://groups.google.com/group/poppr>. You can submit bug
reports there or on our github site: 
<https://github.com/grunwaldlab/poppr/issues>

Acknowledgments
---------------

Much thanks goes to Sydney E. Everhart for alpha testing, beta testing,
feature requests, proofreading, data contribution, and moral support
throughout the writing of this package and manual. Thanks also to Brian
Knaus, Ignazio Carbone, David Cooke, Corine Schoebel, Jane Stewart, and
Zaid Abdo for beta testing and feedback.

The following data sets are included in *poppr*:

-   `Pinf` (SSR, *Phytophthora infestans*)[@goss2014irish]

-   `monpop` (SSR, *Monilinia fructicola*)[@everhart2014finescale]

-   `Aeut` (AFLP, *Aphanomyces eutieches*)[@Grunwald:2006]

-   `Pram` (SSR, *Phytophthora
    ramorum*)[@kamvar2015spatial; @goss2009population]

Citation
--------

To cite *poppr*, please type in your R console:

```{r popprcite, eval = TRUE}
citation(package = "poppr")
```

The formal publication for the first version of *poppr* was
published in the journal **PeerJ**: <http://peerj.com/articles/281/>
[@kamvar2014poppr]. The new features in version 2.0 were published in
**Frontiers** <http://dx.doi.org/10.3389/fgene.2015.00208>
[@kamvar2015novel].

Installation and Quick Start
============================

Installation {#intro:install}
------------

This manual assumes you have installed R. If you have not, please refer
to The CRAN home page at <https://cran.r-project.org/>. We also recommend
the Rstudio IDE (<http://www.rstudio.com/>), which allows the user to
view the R console, environment, scripts, and plots in a single window.

### From CRAN

To install *poppr* from CRAN, select "Package Installer"
from the menu "Packages & Data" in the GUI or type:

```{r install, eval = FALSE}
install.packages("poppr", repos = "http://cloud.r-project.org/")
```

All dependencies will also be installed. In the unfortunate case this does not 
work, please consult
<https://cran.r-project.org/doc/manuals/R-admin.html#Installing-packages>.

### From GitHub

GitHub is a repository where you can find all stable and development versions of
*poppr*.

Since *poppr* contains C code, it needs to be compiled, which means that you
need a working C compiler. If you are on Linux, you should have that, but if you
are on Windows or OSX, you might need to download some special tools:

Windows

:   Download Rtools: <https://cran.r-project.org/bin/windows/Rtools/>

OSX

:   Download Xcode: <https://developer.apple.com/xcode>

To install from GitHub, you do not need to download the tarball since there is a
package called *devtools* that will download and install the package for you
directly from GitHub. After you have installed all dependencies (see above
section), you should download *devtools*:

```{r install_devtools, eval = FALSE}
install.packages("devtools")
```

Now you can execute the command `install_github()` with the user and
repository name:

```{r install_github, eval = FALSE}
devtools::install_github("grunwaldlab/poppr")
```

Quick start {#intro:qstart}
-----------

The author assumes that if you have reached this point in the manual, then you
have successfully installed R and *poppr*. Before proceeding, you should be
aware that R is case sensitive. This means that the words "Case" and "case" are
different. You should also know where your R package library is located. In this
section, you will learn how to locate a file, import it to R, and make a first
analysis using the `poppr()` function.

> **What or where is my R package library?** 
>
> R is as powerful as it is through a community of people who submit extra code
> called "packages" to help it do specific things. These packages live in a
> certain place on your computer called an R library. You can find out where
> this library is by typing `.libPaths()`

Importing a file into R involves you knowing the path to your file and then
typing that into R's console. `getfile()` will help provide a point and click
interface for selecting a file. This is simply a tool to help you get started.
As you get better with R, you might feel that you don"t need it at all.

First, tell your computer to search R's library, load the *poppr* package, and
use `getfile()`:

```{r echo = FALSE}
x <- list(files = "/path/to/R/poppr/files/rootrot.csv", path = "/path/to/R/poppr/files")
```

```{r message = TRUE}
library("poppr")
```

```{r getfilefunk, eval=FALSE}
x <- getfile()
```

A pop up window will appear like this[^1]:


```{r getfile-window, out.width = "500px", fig.cap = "A popup window as it appears in OSX (Mountain Lion)", echo = FALSE}
knitr::include_graphics(path = "getfile.png")
```


You should navigate to your R library and select the file called "rootrot.csv".
If you don"t know where your the poppr package lives, you can find it by typing
`find.package("poppr")` into the R command line. Once we select a file, the file
name and its path will be stored in the variable, `x`. We can confirm that by
typing `x` into R"s command line.

```{r getfilex}
x
```

Here we can see that `x` is a list with two entries: `$files` shows the
files you selected and `$path` shows the path to those files.

We will use `x$files` to access the file. The file is in the GenAlEx format, so
we will import it using `read.genalex()` and then analyze it with the function
`poppr()` to get a table of diversity indices per population.


```{r firstpoppr, eval=FALSE}
myData  <- read.genalex(x$files)
myData
```

```{r echo = FALSE}
options(width=90)
myData <- read.genalex(system.file("files/rootrot.csv", package="poppr"))
```

```{r aflp}
popdata <- poppr(myData)
```

The output of `poppr()` was assigned to the variable `popdata`, so let"s
look at the data.

```{r firstpoppr2}
popdata
```


The fields you see in the output include:

-   `Pop -` Population name (Note that "Total" also means "Pooled").

-   `N -` Number of individuals observed.

-   `MLG -` Number of multilocus genotypes (MLG) observed.

-   `eMLG -` The number of expected MLG at the smallest sample size
    $\geq 10$ based on rarefaction. [@Hurlbert:1971]

-   `SE -` Standard error based on `eMLG` [@Heck:1975]

-   `H -` Shannon-Wiener Index of MLG diversity. [@Shannon:1948]

-   `G -` Stoddart and Taylor"s Index of MLG diversity. [@Stoddart:1988]

-   `lambda -` Simpson"s index, $\lambda$.

-   `E.5 -` Evenness, $E_5$.
    [@Pielou:1975][@Ludwig:1988][@Grunwald:2003]

-   `Hexp -` Nei"s 1978 Expected Heterozygosity. [@Nei:1978]

-   `Ia -` The index of association, $I_A$. [@Brown:1980] [@Smith:1993]
    [@Agapow:2001]

-   `rbarD -` The standardized index of association, $\bar{r}_d$.
    [@Agapow:2001]

These fields are further described in the function `poppr`. You can access the
help page for `poppr` by typing `?poppr` in your R console.

One thing to note about this output is the `NaN` in the column labeled `SE`. In
R, `NaN` means "Not a number". This is produced from calculation of a standard
error based on rarefaction analysis. Occasionally, this calculation will
encounter a situation in which it must attempt to take a square root of a
negative number. Since the root of any negative number is not defined in the set
of real numbers, it must therefore have an imaginary component, $i$.
Unfortunately, R will not represent the imaginary components of numbers unless
you specifically tell it to do so. By default, R represents these as `NaN`.


Import and Data types
=====================

Importing data into poppr {Get out of my dreams and into my R} {#intro:import}
--------------------------------------------------------------

### How does R represent data? {Objective: data}

Working with data in R means that these data have to be stored inside an
"object", which is stored in the computer"s memory. Because of this, it"s
important to know the difference between a **file** and an **object**. When
anyone talking about importing a **file** into R, they are referring to a
spreadsheet or text file that lives in a folder on your hard drive. Spreadsheet
files (saved as \*.csv files) are normally imported through the R function
`read.table()`. The output of `read.table()` is a `data.frame`. A `data.frame`
is an **object** represented in your computer's memory. This means that it only
exists for as long as R is running.

The good thing about having objects stored in memory is that you can manipulate
them in any way and not affect the source of those data. Since R is a scripted
language (instead of point-and-click), any of your manipulations can be saved in
a separate R file that can be easily adapted to new data. Of course, most data
are not going to be entered into R manually. Usually they will be formatted in a
manner that can be read by popular population genetics programs.

As previously mentioned, since *poppr* is based on *adegenet*, it's possible to
read in the following file formats into a `genind` object with the function
`import2genind()`:

- **Fstat**

- **Genepop**

- **Genetix**

- **Structure**

Here, we introduce a new way of importing data into a `genind` or `genclone`
object from a GenAlEx formatted file.

### Function: read.genalex {#intro:import:read.genalex}

A very popular program for population genetics is GenAlEx
(<http://biology.anu.edu.au/GenAlEx/Welcome.html>) [@Peakall:2012;
@Peakall:2006]. GenAlEx runs within the Excel environment and can be very
powerful in its analyses. *Poppr* has added the ability to read \*.CSV files[^2]
produced in the GenAlEx format. It can handle data types containing regions and
geographic coordinates, but currently cannot import allelic frequency data from
GenAlEx. Using the *poppr* function `read.genalex()` will import your data into
*adegenet*"s `genind` object or *poppr*"s `genclone` object (more information on
that below). For ways of formatting a GenAlEx file, see the manual here:
<http://biology.anu.edu.au/GenAlEx/Download_files/GenAlEx%206.5%20Guide.pdf>

Below is an example of the GenAlEx format. We will use the data set
called `microbov` from the *adegenet* package to generate
it. The data contains three demographic factors: Country, Species and
Breed contained within the `@other` slot (detailed in [the 'other' slot](#intro:genind:other)). We will first set
these as the population strata, define the population as the combination
of the strata, and then save a file to the desktop.

```{r microsave, eval=FALSE}
library("poppr")
data(microbov)
strata(microbov) <- data.frame(other(microbov)) # set the strata
microbov
genind2genalex(microbov, file = "~/Desktop/microbov.csv")
```

```{r microsave_message, echo=FALSE}
cat("Extracting the table ... Writing the table to ~/Desktop/microbov.csv ... Done.")
```

```{r microbov-unmodified, out.width = "700px", fig.cap = paste(mictext), echo = FALSE}
mictext <- paste(
  "The first 15 individuals and 4 loci of the microbov data set. The",
  "first column contains the individual names, the second column contains",
  "the population names, and each subsequent column represents",
  "microsatellite genetic data. Highlighted in red is a list of populations",
  "and their relative sizes."
  )
knitr::include_graphics(path = "unmod_dat.png")
```

The GenAlEx format contains individuals in rows and loci in columns.
Individual data begins at row 4. Column A always contains individual
names and column B defines the population of each individual. Notice
here that the three demographic factors from the data have been
concatenated with a "\_". This allows us to import more than one
population factor to use as hierarchical levels in a `genclone` object.

The First three rows contain information pertaining to the global data
set. The only important information for *poppr* is the
information contained in row 3 and the first three columns of row 1.

          **A**        **B**               **C**               **D**       
  ------- ------------ ------------------- ------------------- ----------- -----
  **1**   \# of Loci   \# of Individuals   \# of Populations   Pop1 Size   ...
  **2**   -            -                   -                   Pop1 Name   ...
  **3**   -            -                   Locus 1             ...         

Highlighted in red in figure above are definitions of
the number of populations and their respective sizes. As this is
redundant information, we can remove it. Below is an example of a valid
data set that can be imported into *poppr*.

```{r microbov-modified, out.width = "700px", fig.cap = paste(mictext), echo = FALSE}
mictext <- paste(
  "The first 15 individuals and 4 loci of the microbov data set. This is the same",
  "figure as above, however the populations and counts have been removed from the",
  "header row and the third number in the header has been replaced by 1" 
  )
knitr::include_graphics(path = "mod_dat.png")
```

All GenAlEx formatted data can be imported with the command
`read.genalex()`, detailed below:

------------------------------------------------------------------------


```{r read.genalex_command, echo = FALSE, comment = NA, size = 'normalsize'}
funk <- "read.genalex"
print_command(funk)
```

-   `genalex -` a \*.CSV file exported from GenAlEx on your disk (For
    example: `my_genalex_file.csv`).

-   `ploidy -` a number indicating the ploidy for the data set (eg 2 for
    diploids, 1 for haploids).

-   `geo -` GenAlEx allows you to have geographic data within your file.
    To do this for *poppr*, you will need to follow the
    first format outlined in the GenAlEx manual and place the geographic
    data AFTER all genetic and demographic data with one blank column
    separating it (See the GenAlEx Manual for details). If you have
    geographic information in your file, set this flag to `TRUE` and it
    will be included within the resulting genind object in the
    `@other` slot. (If you don"t know what that is, don"t worry. It will
    be explained later in [the 'other' slot](#intro:genind:other))

-   `region -` To format your GenAlEx file to include regions, you can
    choose to include a separate column for regional data, or, since
    regional data must be in contiguous blocks, you can simply format it
    in the same way you would any other data (see the GenAlEx manual
    for details). If you have your file organized in this manner, select
    this option and the regional information will be stored in the
    `@other` slot of the resulting `genind` object or be incorporated
    into the hierarchy of the `genclone` object.

-   `genclone -` This flag will convert your data into a `genclone`
    object (see [Send in the clones](#intro:genclone) for more info).

-   `sep -` The separator argument for columns in your data. It defaults
    to ",".

-   `recode -` If your data is polyploid data, this gives you the option
    to recode it. (See [About Polyploid Data](#intro:import:polyploid) for details)

------------------------------------------------------------------------

Note that regional data and geographic data are not mutually exclusive.
You can have both in one file, just make sure that they are on the same
sheet and that the geographic data is always placed after all genetic
and demographic data.

We have a short example of GenAlEx formatted data with no
geographic or regional formatting. We will first see where the data is
using the command `system.file()`

```{r system_file_genalex, eval=FALSE}
system.file("files/rootrot.csv", package="poppr")
```

```{r system_file_echo, echo=FALSE}
paste("/path/to/R/library/poppr/files/rootrot.csv")
```

Now import the data into *poppr* like so:

```{r read.genalex_ex}
rootrot <- read.genalex(system.file("files/rootrot.csv", package="poppr"))
```

Executing rootrot shows that this file is now in `genclone` format and
can be used with any function in *poppr* and *adegenet*

```{r read.genalex_ex2}
rootrot
```

### Other ways of importing data {#intro:import:other}

*Adegenet* already supports the import of 
**FSTAT**, 
**Structure**, 
**Genpop**, and 
**Genetix** formatted files, so if
you have data in those formats, you can import them using the function
`import2genind`. For sequence data, check if you can use `read.dna()` from
the *ape* package to import your data. If you can, then you can use the
*adegenet* function `DNAbin2genind()`. If you don"t have any
of these formats handy, you can still import your data using R"s
`read.table` along with `df2genind()` from *adegenet*. For
more information, see *adegenet*'s "Getting Started"
vignette.

### Function: genind2genalex (exporting data) {#intro:import:genind2genalex}

Of course, being able to export data is just as useful as being able to
import it, so we have this handy little function that will write a
GenAlEx formatted file to wherever you desire.\
**WARNING: This will overwrite any file that exists with the same
name.**

------------------------------------------------------------------------

```{r genind2genalex_command, echo = FALSE, comment = NA, size = 'normalsize'}
funk <- "genind2genalex"
print_command(funk)
```

-   `pop -` a `genind` object.

-   `filename -` This is where you specify the path to the new file you
    wish to create. If you specify only a filename with no path, it will
    place the file in your current working directory.

-   `quiet -` If this is set to `FALSE`, a status message will be
    printed to the console as the extraction progresses.

-   `pop -` A vector specifying a custom population OR a formula
    specifying the strata to be combined in the new file.

-   `allstrata -` This is `TRUE` by default and will combine all of the
    strata in your file unless you have specified a new
    population factor.

-   `geo -` Set to `TRUE`, if you have a data frame or matrix in the
    `@other` slot of your `genind` object that contains geographic
    coordinates for all individuals or all populations. Setting this to
    `TRUE` means the resulting file will have two extra columns at the
    end of your file with geographic coordinates.

-   `geodf -` The name of the data frame or matrix containing the
    geographic coordinates.

-   `sep -` A separator to separate columns in the resulting file.

------------------------------------------------------------------------

First, a simple example for the rootrot data we demonstrated in an earlier section:

```{r genind2genalex, eval=FALSE}
genind2genalex(rootrot, "~/Desktop/rootrot.csv")
```

```{r genind2genalex_cat, echo=FALSE}
cat("Extracting the table ... Writing the table to ~/Desktop/rootrot.csv ... Done.\n")
```


Here's an example of exporting the nancycats data set into GenAlEx format with
geographic information. If we look at the nancycats geographic information, we
can see it"s coordinates for each population, but not each individual:

```{r nancyxy}
data(nancycats)
other(nancycats)$xy
```

To export it:

```{r genind2genalex_nancy, eval=FALSE}
genind2genalex(nancycats, "~/Desktop/nancycats_pop_xy.csv", geo = TRUE)
```

```{r genind2genalex_cat2, echo=FALSE}
cat("Extracting the table ... Writing the table to ~/Desktop/nancycats_pop_xy.csv ... Done.\n")
```

If we wanted to assign a geographic coordinate to each individual, we
can use this trick knowing that there are 17 populations in the data
set:

```{r nancy_grow_xy}
nan2           <- nancycats
other(nan2)$xy <- other(nan2)$xy[pop(nan2), ]
tail(other(nan2)$xy)
```

Now we can export it to a different file.

```{r genind2genalex_nancyind, eval=FALSE}
genind2genalex(nan2, "~/Desktop/nancycats_inds_xy.csv", geo = TRUE)
```

```{r genind2genalex_cat2ind, echo=FALSE}
cat("Extracting the table ... Writing the table to ~/Desktop/nancycats_inds_xy.csv ... Done.\n")
```

Getting to know *adegenet*"s `genind` object {#intro:genind}
---------------------------------------------------------

Since *poppr* was built around *adegenet*"s
framework, it is important to know how *adegenet* stores
data in the `genind` object, as that is the object used by
*poppr*. To create a `genind` object,
*adegenet* takes a data frame of genotypes (rows) across
multiple loci (columns) and converts them into a matrix of individual
allelic counts at each locus [@Jombart:2008].

For example, Let's say we had data with 3 diploid individuals each with
3 loci that had 3, 4, and 5 allelic states respectively:

      locus1    locus2    locus3
  --- --------- --------- ---------
    1 101/101   201/201   301/302
    2 102/103   202/203   301/303
    3 102/102   203/204   304/305



The resulting `genind` object would contain a matrix that has 3 rows and
12 columns. Below is a schematic of what that would look like. Each
column represents a separate allele, each row represents an individual
and each color represents a different locus.

```{r ex_data_picture, echo = FALSE, fig.width=14, fig.height=4, out.width = "700px"}
library("adegenet")
df <- data.frame(list(locus1=c("101/101", "102/103", "102/102"),
                      locus2=c("201/201", "202/203", "203/204"),
                      locus3=c("301/302", "301/303", "304/305")))
dat <- tab(df2genind(df, sep="/"))
tdat <- dat
tdat[] <- 1
x <- barplot(tdat, axes = FALSE, axisnames = FALSE)
barplot(rep(3, 12), col = rep(rainbow(3, alpha = 0.5), 3:5), axes = FALSE, add = TRUE)
axis(2, at = 1:3 - 0.5, labels = 1:3, tick = FALSE)
axis(3, at = x, labels = colnames(tdat), tick = FALSE)
axis(1, at = c(2, 6.125, 11.5), labels = names(df), tick = FALSE)
```

When we look at the data derived from table above, we see
that we have a matrix of individual allele counts at each locus.

```{r ex_genind, echo=FALSE}
dat
```

At each locus, the allele counts for each individual sum to the ploidy, $p$.
Homozygotes are denoted as having an allele count of $p$ at a single allele
within a locus, while heterozygotes have their allele counts represented as $<p$
where $p$ = ploidy across multiple alleles in a single locus. Along with this
matrix, are elements that define the names of the individuals, loci, alleles,
and populations. If you wish to know more, see the *adegenet* "Getting Started"
manual.

### The other slot {#intro:genind:other}

The other slot is a place in the `genind` object that can be used to
store useful information about the data. We saw earlier that it could
store demographic information, now let's explore a different example.
Bruvo's distance is based off of a stepwise mutation model for
microsatellites. This requires us to know the length of the repeat of
each locus. We could store the repeat lengths in a separate variable in
our R environment, but we are at risk of losing that. One way to prevent
it from being lost would be to place it in the "other" slot. For the
purpose of this example, we will use the "nancycats" data set from the
*adegenet* package and assume that it has di-nucleotide
repeats at all of its loci.

```{r other_replen}
data(nancycats) # Load the data
other(nancycats) # geographical coordinates
repeats <- rep(2, nLoc(nancycats)) #nLoc = number of loci
repeats
other(nancycats)$repeat_lengths <- repeats
other(nancycats) # two items named xy and repeat_lengths
```

The `genclone` object {send in the clones} {#intro:genclone}
------------------------------------------

The `genclone` class was defined in order to make working with clonal
organisms more intuitive. It is built off of the `genind` object and has
dedicated slots for defined multilocus genotypes. The name `genclone`
refers to the fact that it has the ability to handle genotypes of clonal
organisms (but it is also used for sexual populations).

In previous versions of *poppr*, the `genclone` object
contained a hierarchy slot as well. This slot was moved to
*adegenet* and its name was changed to "strata". This slot
allows you to carry around several definitions for populations in the
same data set.

The function `as.genclone` allows the user to convert a `genind` object
to a `genclone` object. The following example will demonstrate that the
`genclone` object is an extension of the `genind` object as well as the
advantages of having populations pre-defined in your data set.

### Function: as.genclone {#intro:genclone:as.genclone}

------------------------------------------------------------------------

```{r as.genclone_command, echo = FALSE, comment = NA, size = 'normalsize'}
funk <- "as.genclone"
print_command(funk)
```

-   **x -** a `genind` object to be converted.

-   **... -** any arguments to be passed to the `genind` constructor.

-   **mlg -** a vector representing the multilocus genotype definitions
    of your data.

-   **mlgclass -** if `TRUE`, the MLGs represented in your object will
    be converted to an MLG class object, which allows for custom
    MLG definitions.

------------------------------------------------------------------------

Let"s show an example of a `genclone` object. First, we will take an existing
`genind` object and convert it using the function `as.genclone` (We can also use
the function [read.genalex()](#intro:import:read.genalex)to import as `genclone`
or `genind` objects). We will use the `Aeut` data set because it is a clonal
data set that has a simple population strata [@Grunwald:2006]. The data set is
here: <http://dx.doi.org/10.6084/m9.figshare.877104> and it is AFLP data of the
root rot pathogen *Aphanomyces euteiches* collected from two different fields in
NW Oregon and W Washington, USA. These fields were divided up into subplots from
which samples were collected. The fields represent the population and the
subplots represent the subpopulation. Let"s take a look at what the `genind`
object looks like:

```{r show_genind}
library("poppr")
data(Aeut)
Aeut
```

We can see that there is a data frame in the `@other` slot called
"population\_hierarchy". This is left over from *poppr* version 1.x behavior.
Since the `genind` object now has a `@strata` slot, we can use it to set the
stratification (previously called "hierarchy").

```{r add_strata_Aeut}
strata(Aeut) <- other(Aeut)$population_hierarchy[-1]
Aeut
```

Now we can convert this to a `genclone` object, which will store
information about our multilocus genotypes for us.

```{r as_genclone}
agc <- as.genclone(Aeut)
agc
```

If we wanted to, we could also convert it back to a `genind` object.


```{r genclone2genind}
genclone2genind(agc)
```

About polyploid data {#intro:import:polyploid}
--------------------

> **WARNING**
>
> Treat polyploid data with care. Please read this section carefully and consult
> the help pages for all functions mentioned here.

With diploid or haploid data, genotypes are unambiguous. It is often
clear when it is homozygous or heterozygous. With polyploid data,
genotypes can be ambiguous. For example, a tetraploid individual with
the apparent genotype of **A/B** could actually have one of three
genotypes: **A/A/A/B**, **A/A/B/B**, or **A/B/B/B**. This ambiguity
prevents a researcher from accurately calling all alleles present. In
*adegenet*, it was previously difficult to import polyploid
data because of this ambiguity as data was required to be unambiguous or
missing.

A solution to this problem is to code missing alleles as "0". An example
of this is found within the `Pinf` data set in *poppr*
[@goss2014irish]. First, we will look at where we have polyploid allele
calls.

```{r pinf_table, fig.width = 5, fig.height = 15}
data(Pinf)
Pinf
ptab <- info_table(Pinf, type = "ploidy", plot = TRUE)
```

We look at the last six samples over two loci, Pi63 (3 alleles,
triploid) and Pi70 (3 alleles, diploid) to examine how the data is
represented.

```{r pinf_show}
Pinf[loc = locNames(Pinf)[9:10]] %>% tab() %>% tail()
```
Each column in this data represents a different allele at a particular
locus. `Pi63.148` is the allele `148` at locus `Pi63`. Each row is an
individual. The numbers represent the fraction of a given allele that
makes up the individual genotype at a particular locus. What we can see
here is that the number of columns is 8 when we expect only 6 (2 loci
x 3 alleles). The first allele at each locus is `000`. Let's take
a look at the data in a human-readable format.

```{r pinf_show_df}
Pinfdf <- genind2df(Pinf, sep = "/")
tail(Pinfdf[10:11])
```

It"s more clear now that we have a data set of tetraploid individuals
where some genotypes appear diploid (`000/000/157/157`) and some appear
triploid (`000/148/151/157`). The tetraploid genotype is padded with
zeroes to make up the difference in ploidy.

This method allows Bruvo's Distance [@Bruvo:2004] and the Index of Association
[@Brown:1980; @Smith:1993; @Agapow:2001] to work with polyploids as they
specifically recognize the zeroes as being missing data. A side effect,
unfortunately is that the extra zeroes appear as extra alleles. As this
affects all frequency-based statistics (except for the ones noted
above), the user should reformat their data set with the function
`recode_polyploids`, which will remove the zeroes.


```{r pinf_recode}
Pinf_rc <- recode_polyploids(Pinf, newploidy = TRUE)
Pinf_rc # Notice that the new ploidy is accounted for.
Pinf_rc[loc = locNames(Pinf_rc)[9:10]] %>% tab() %>% tail
```

Below, we show the observed genotypes:

```{r pinf_recode_df}
Pinfrcdf <- genind2df(Pinf_rc, sep = "/")
tail(Pinfrcdf[10:11])
```

If you have imported your data as recoded polyploid data, you can use
the argument "addzero" to fill out the ploidy:

```{r pinf_rerecode}
Pinf_rc[loc = locNames(Pinf_rc)[9:10]] %>%
  recode_polyploids(addzero = TRUE) %>%
  tab() %>%
  tail()
```

Data Manipulation {#data.manip}
=================

One tedious aspect of population genetic analysis is the need for
repeated data manipulation. *Poppr* includes novel
functions for clone- censoring your data sets, removing genotypes or
loci with missing data, removing uninformative loci, and shuffling
populations.

Replace or remove missing data {Inside the golden days of missing data} {#data.manip:missing}
-----------------------------------------------------------------------

A data set without missing data is always ideal, but often not
achievable. The *poppr* function `missingno` exists to
handle missing data. Missing data can mean different things based on
your data type. For microsatellites, missing data might represent any
source of error that could cause a PCR product to not amplify in gel
electrophoresis, which may or may not be biologically relevant. For a
DNA alignment, missing data could mean something as simple as an
insertion or deletion, which is biologically relevant. The choice to
exclude or estimate data has very different implications for the type of
data you have.

> **Treatment of Missing data is a non-trivial task.** You should understand the
> nature of missing data in your data set before treatment.


------------------------------------------------------------------------

### Function: missingno {#data.manip:missing:missingno}

`missingno` is a function that serves as a way to exclude specific areas
that contain systematic missing data. There are four methods available,

```{r missingo_command, echo = FALSE, comment = NA, size = 'normalsize'}
funk <- "missingno"
print_command(funk)
```
`pop -` a `genind` object.

`type -` This could be one of five options:

 (@) "loci" This is to be used for a data set that has systematic
problems with certain loci that contain null alleles or simply failed to
amplify. This will remove loci with a defined threshold of missing data
from the data set.

 (@) "geno" This is to be used for genotypes (individuals) in your data
set where many null alleles are present. Individuals with a defined
threshold missing data will be removed.

 (@) "asis" This argument will retain missing data in your data set. It"s useful for
functions that utilize `missingno` internally, such as `mlg.filter()`,
`poppr.amova()`, or `aboot()`.

 (@) "mean" This replaces missing data with the mean allele frequencies in the
entire data set .

 (@) "zero" or "0" This replaces missing data with zero, signifying a new
allele .

`cutoff -` This is a numeric value from 0 to 1 indicating the percent
allowable missing data for either loci or genotypes. If you have, for
example, two loci containing missing 5% and 10% missing data,
respectively and you set `cutoff = 0.05`, `missingno` will remove the
second locus. Percent missing data for genotypes is considered the
percent missing loci over number of total loci.

`quiet -` When this is set to `FALSE`, the number of missing values
replaced will be printed to screen if the method is "zero" or "mean". It
will print the number of loci or individuals removed if the method is
"loci" or "geno".

`freq -` This is used for compatibility with the `tab()` method for
`genind` object. It will convert counts of alleles to frequencies of
alleles, rendering an object that will return warnings.

------------------------------------------------------------------------

Let's take a look at what this does by focusing in on areas with missing
data. We"ll use the data set `nancycats` as an example. Using the
*poppr* function `info_table`, we can assess missing data
within populations.

```{r initializing_poppr, fig.height = 6, fig.width = 10, out.width = "700px"}
library("poppr")
data(nancycats)
info_table(nancycats, plot = TRUE)
```



We can see that locus fca8 has a lot of missing data. To demonstrate the
function `missingno()`, we will zoom into the first five individuals
at the first locus.

```{r nancy_indiv}
tab(nancycats)[1:5, 8:13]
```

When looking at this data set, recall how a `genind` object is
formatted. You have a matrix representing counts of alleles. For
diploids, if you see 1, that means it is heterozygous at that allele,
and a 2 means it"s homozygous. Here we see three heterozygotes and two
individuals with missing data (indicated by NA). Let"s look at what
happens when we exclude loci and genotypes with &gt;5% missing data).

```{r missingno_exclude}
nanloci <-  missingno(nancycats, "loci")
nangeno <-  missingno(nancycats, "geno")
tab(nanloci)[1:5, 8:13]
```

Notice how we now see columns named fca23.128 and fca23.130. This is
showing us another locus because we have removed the first. Recall from
the summary table that the first locus had 16 alleles, and the second
had 11. Now that we've removed loci containing missing data, all others
have shifted over.    
Let's look at the loci names and number of individuals.

```{r missingno_loci}
nInd(nanloci)     # Individuals
locNames(nanloci) # Names of the loci
```

You can see that the number of individuals stayed the same but the loci
"fca8", "fca45", and "fca96" were removed.
Let's look at what happened when we removed individuals.

```{r missingno_geno}
tab(nangeno)[1:5, 8:13]
nInd(nangeno)     # Individuals
locNames(nangeno) # Names of the loci
```

We can see here that the number of individuals decreased, yet we have
the same number of loci. Notice how the frequency matrix changes in both
scenarios? In the scenario with "loci", we removed several columns of
the data set, and so with our sub-setting, we see alleles from the
second locus. In the scenario with "geno", we removed several rows of
the data set so we see other individuals in our sub-setting.

Extract populations {Divide (populations) and conquer (your analysis)} {#data.manip:divide}
----------------------------------------------------------------------

This *poppr* function `popsub` makes subsetting `genind` or
genlight objects by population easier:

### Function: popsub {#data.manip:divide:popsub}

The command `popsub` is powerful in that it allows you to choose exactly
what populations you choose to include or exclude from your analyses. As
with many R functions, you can also use this within a function to avoid
creating a new variable to keep track of.

------------------------------------------------------------------------

```{r popsub_command, echo = FALSE, comment = NA}
funk <- "popsub"
print_command(funk)
```

-   `pop -` a `genind` object.

-   `sublist -` vector of populations or integers representing the
    populations in your data set you wish to **retain**. For example:
    `sublist = c(pop_z, pop_y)` or `sublist = 1:2`.

-   `blacklist -` vector of populations or integers representing the
    populations in your data set you wish to **exclude**. This can take
    the same type of arguments as sublist, and can be used in
    conjunction with sublist for when you want a range of populations,
    but know that there is one in there that you do not want to analyze.
    For example: `sublist = 1:15, blacklist = pop_x`. One very useful
    thing about the blacklist is that it allows the user to be extremely
    paranoid about the data. You can set the blacklist to contain
    populations that are not even in your data set and it will still
    work!

-   `mat -` A matrix produced from the `mlg.table` function. This overrides the *pop* argument and subsets
    this table instead.

------------------------------------------------------------------------

To demonstrate this tool, we'll use the H3N2 virus data set provided in
*adegenet*. It contains a data frame in the "other" slot
called "x" that contains information about the year of epidemic,
country, etc.

```{r load_H3N2}
data("H3N2", package = "adegenet")
strata(H3N2) <- data.frame(other(H3N2)$x)
H3N2
```

We will demonstrate the `popsub` functionality by setting the population
factor to "country". Note, in this section, I am naming the variables
staring with "v" indicating "virus".

```{r popsub_sublist}
setPop(H3N2) <- ~country
popNames(H3N2) # Only two countries from North America.
v_na <- popsub(H3N2, sublist = c("USA", "Canada"))
popNames(v_na)
```

If we want to see the population size, we can use the
*adegenet* function `nInd()`:

```{r popsub_sizes}
c(NorthAmerica = nInd(v_na), Total = nInd(H3N2))
```

You can see that the population factors are correct and that the size of
the data set is considerably smaller. Let's see the data set without the
North American countries.

```{r popsub_blacklist}
v_na_minus <- popsub(H3N2, blacklist = c("USA", "Canada"))
popNames(v_na_minus)
```
Let"s make sure that the number of individuals in both data sets is
equal to the number of individuals in our original data set:

```{r length_test}
(nInd(v_na_minus) + nInd(v_na)) == nInd(H3N2)
```

Now we have data sets with and without North America. Let's try
something a bit more challenging. Let's say that we want the first 10
populations in alphabetical order, but we know that we still don"t want
any countries in North America. We can easily do this by using the
*base* function `sort`.

```{r popsub_combine}
vsort <- sort(popNames(H3N2))[1:10]
vsort
valph <- popsub(H3N2, sublist = vsort, blacklist = c("USA", "Canada"))
popNames(valph)
```

And that, is how you subset your data with *poppr*!

Clone-censor data sets {Attack of the clone correction} {#data.manip:cc}
-------------------------------------------------------

Clone correction refers to the ability of keeping one observation of
each MLG in a given population (or sub-population). Clone correcting can
be hazardous if its done by hand (even on small data sets) and it
requires a defined population hierarchy to get relevant results.
*Poppr* has a clone correcting function that that will
correct down to the lowest level of any defined population hierarchy.
Note that clone correction in *poppr* is sensitive to
missing data, as it treats all missing data as a single extra allele.

This function will create new data sets, but it is also utilized by the
functions `poppr()` and `poppr.amova()` natively.

### Function: clonecorrect {#data.manip:cc:clonecorrect}

This function will return a clone corrected data set corrected for the
lowest population level. Population levels are specified with the `hier`
flag. You can choose to combine the population hierarchy to analyze at
the lowest population level by choosing `combine = TRUE`.

------------------------------------------------------------------------

```{r clonecorrect_command, echo = FALSE, comment = NA, size = 'normalsize'}
funk <- "clonecorrect"
print_command(funk)
```

-   `pop -` a `genclone` object with a defined hierarchy or a `genind`
    object that has a population hierarchy data frame in the
    `@other` slot. Note, the `genind` object does not necessarily
    require a population factor to begin with.

-   `strata -` A hierarchical formula (eg. `\simPop/Subpop`),
    representing the hierarchical levels in your data.

-   `combine -` Do you want to combine the population hierarchy? If it"s
    set to `FALSE` (default), you will be returned an object with the
    top most hierarchical level as a population factor unless the
    **keep** argument is defined. If set to `TRUE`, the hierarchy will
    be returned combined.

-   `keep -` This flag is to be used if you set `combine = FALSE`. This
    will tell clone correct to return a specific combination of your
    hierarchy defined as integers. For example, imagine a hierarchy that
    needs to be clone corrected at three levels: *Population* by *Year*
    by *Month*. If you wanted to only run an analysis on the
    *Population* level, you would set `keep = 1` since *Population* is
    the first level of the hierarchy. On the other hand, if you wanted
    to run analysis on *Year* by *Month*, you would set `keep = 2:3`
    since those are the second and third levels of the hierarchy.

------------------------------------------------------------------------

Let"s look at ways to clone-correct our data. We"ll look at our *A.
euteichies* data that we used in an earlier section since that data set is
known to include clonal populations [@Grunwald:2006]. Try playing around
with the data and see what different combinations of the `strata`, and
`keep` flags produce. Below, I will give three examples of clone
corrections at the sample level with respect to field, at the field
level, and finally, at the level of the entire data set.

First, we will examine the original data set.

```{r clonecorrect}
data(Aeut)
strata(Aeut) <- data.frame(other(Aeut)$population_hierarchy[-1])
Aeut
```

When you read in data with `read.genalex()`, the default is to represent
it in a `genclone` object. Since the `clonecorrect()` function works on
multilocus genotype definitions, It's more efficient to convert it to a
`genclone` object first. We will also rename the strata to "field" and
"sample" to make the biological relevance of the data clearer.

```{r clonecorrect_genclone}
aphan <- as.genclone(Aeut)
nameStrata(Aeut) <- ~field/sample
```

Now we correct by sample with respect to field and keep the field as the
population.

```{r clonecorrect2}
clonecorrect(aphan,  strata = ~Pop/Subpop)
# Your turn: Use the same stratification and use combine = TRUE and then
# keep = 1:2. Is there any difference?
```

Correcting by field. Notice how the number of MLG is much closer to our
census.

```{r clonecorrect3}
clonecorrect(aphan, strata = ~Pop)
```

Correcting over whole data set. Our MLG is equal to our census.

```{r clonecorrectx}
clonecorrect(aphan, strata = NA)
```

Permutations and bootstrap resampling {every day I'm shuffling (data sets)} {#data.manip:shuffle}
---------------------------------------------------------------------------

A common null hypothesis for populations with mixed reproductive modes
is panmixia, or to put it simply: lots of sex. *Poppr*
randomly shuffles data sets in order to calculate P-values for the index
of association ($I_A$ and $\bar{r} _d$) [@Agapow:2001] using 4 different
methods:

    method strategy      units sampled
  -------- ------------- ---------------
         1 permutation   alleles
         2 simulation    alleles
         3 simulation    alleles
         4 permutation   genotypes

These methods are detailed below. We will create a dummy data set to be
shuffled by each example below. Let"s assume a single diploid locus with
four alleles (1, 2, 3, 4) with the frequencies of 0.1, 0.2, 0.3, and
0.4, respectively:

       A1/A2
  --- -------
  1     4/4
  2     4/1
  3     4/3
  4     2/2
  5     3/3

  : Original<span data-label="tab:original"></span>

The 4 methods are detailed below.

------------------------------------------------------------------------

### Function: shufflepop {#data.manip:shuffle:shufflepop}

```{r shufflepop_command, echo = FALSE, comment = NA, size = 'normalsize'}
funk <- "shufflepop"
print_command(funk)
```

-   `pop -` a `genind` object.

-   `method -` a number indicating the method of sampling you wish
    to use. The following methods are available for use:

    1.  **Permute Alleles (default)** This is a sampling scheme that
        will **permute alleles within the locus.** 
        
               A1/A2
          --- -------
          1     3/4
          2     2/3
          3     4/4
          4     2/1
          5     3/4


               A1/A2
          --- -------
          1     1/3
          2     2/4
          3     3/4
          4     4/3
          5     4/2

        As you can see, The heterozygosity has changed, yet the allelic
        frequencies remain the same. Overall this would show you what
        would happen if the sample you had underwent panmixis within
        this sample itself.

    2.  **Parametric Bootstrap** The previous scheme reshuffled the
        observed sample, but the parametric bootstrap **draws samples
        from a multinomial distribution using the observed allele
        frequencies as weights.**


               A1/A2
          --- -------
          1     1/3
          2     3/3
          3     3/2
          4     4/4
          5     4/2


               A1/A2
          --- -------
          1     3/4
          2     2/3
          3     4/2
          4     4/4
          5     4/2

        Notice how the heterozygosity has changed along with the allelic
        frequencies. The frequencies for alleles 3 and 4 have switched
        in the first data set, and we"ve lost allele 1 in the second
        data set purely by chance! This type of sampling scheme attempts
        to show you what the true population would look like if it were
        panmictic and your original sample gave you a basis for
        estimating expected allele frequencies. Since estimates are made
        from the observed allele frequencies, small samples will produce
        skewed results.

    3.  **Non-Parametric Bootstrap** The third method is sampling with
        replacement, again **drawing from a multinomial distribution,
        but with no assumption about the allele frequencies**.


               A1/A2
          --- -------
          1     1/3
          2     3/3
          3     3/1
          4     2/2
          5     3/1


               A1/A2
          --- -------
          1     1/3
          2     3/1
          3     2/3
          4     2/1
          5     4/3

        Again, heterozygosity and allele frequencies are not maintained,
        but now all of the alleles have a 1 in 4 chance of being chosen.

    4.  **Multilocus permutation** This is called Multilocus permutation
        because it does the same thing as the permutation analysis in
        the program *multilocus* by Paul Agapow and Austin Burt
        [@Agapow:2001]. This will shuffle the genotypes at each locus.
        
               A1/A2
          --- -------
          1     3/3
          2     4/1
          3     2/2
          4     4/4
          5     4/3


               A1/A2
          --- -------
          1     4/4
          2     2/2
          3     3/3
          4     4/3
          5     4/1

        Note that you have the same genotypes after shuffling, so at
        each locus, you will maintain the same allelic frequencies
        and heterozygosity. So, in this sample, you will only see a
        homozygote with allele 2. This also ensures that the P-values
        associated with $I_A$ and $\bar{r} _d$ are exactly the same.
        This method **assumes that alleles are not independently
        assorting within individuals.** This strategy is **useful if you
        suspect the population is inbreeding** (Jerome Goudet,
        personal communication).

------------------------------------------------------------------------

These shuffling schemes have been implemented for the index of
association, but there may be other summary statistics you can use
`shufflepop` for. All you have to do is use the function `replicate`.
Let"s use average Bruvo"s distance with the first population of the data
set `nancycats` as an example:

```{r shuffle_bd}
data(nancycats)
nan1 <- popsub(nancycats, 1)
reps <- rep(2, 9) # Assuming dinucleotide repeats.
observed <- mean(bruvo.dist(nan1, replen = reps))
observed
```

You could use this method to replicate the resampling 999 times and then
create a histogram to visualize a distribution of what would happen
under different assumptions of panmixia.

```{r shuffle_bd_replicate_dummy, eval=FALSE}
set.seed(9999)
bd.test <- replicate(999, mean(bruvo.dist(shufflepop(nan1, method = 2), replen = reps)))
```
```{r shuffle_bd_replicate, echo = FALSE}
load("nancybruvo.rda")
```

```{r bd_histogram, resolution = 300}
hist(bd.test, xlab = "Bruvo's Distance", main = "Average Bruvo's distance over 999 randomizations")
abline(v = observed, col = "red")
legend('topleft', legend="observed", col="red", lty = 1)
```

Removing uninformative loci {Cut It Out!} {#data.manip:informloci}
-----------------------------------------

Phylogenetically uninformative loci are those that have only one sample
differentiating from the rest. This can lead to biased results when
using multilocus analyses such as the index of association
[@Brown:1980; @Smith:1993]. These nuisance loci can be removed with the
following function.

### Function: informloci {#data.manip:informloci:informloci}

------------------------------------------------------------------------

```{r informloci_command, echo = FALSE, comment = NA, size = 'normalsize'}
funk <- "informloci"
print_command(funk)
```

-   `pop -` a `genind` object.

-   `cutoff -` this represents the minimum fraction of individuals
    needed for a locus to be considered informative. The default is set
    to $2/n$ with $n$ being the number of individuals in the data set
    (represented by the *adegenet* function `nInd`).
    Essentially, this means that any locus with fewer than 2
    observations differing will be removed. The user can also specify a
    fraction of observations for the cutoff (eg. 0.05).

-   `MAF -` (Minor Allele Frequency), This defaults to 0.01 indicating
    that loci that contain one allele representing 1 - MAF of the data
    will be removed.

-   `quiet -` if `TRUE`, nothing will be printed to the screen, if
    `FALSE` (default), the cutoff value in percentage and number of
    individuals will be printed as well as the names of the
    uninfomrative loci found.

------------------------------------------------------------------------

Here's a quick example using the H3N2 virus SNP data set from an earlier section. 
We will only retain loci that have a minor allele frequency of $\geq 5\%$

```{r inform.H3N2.1, eval = FALSE}
H.five <- informloci(H3N2, cutoff = 0.05)
```

```{r inform.H3N2.2, echo = FALSE, message = TRUE}
msg <- readLines("msg.txt")
message(paste(msg, collapse = "\n"))
```

Now what happens when you have all informative loci. We"ll use the
nancycats data set, which has microsatellite loci. It is important to
note that this is searching for loci with a specified genotype frequency
as fixed heterozygous sites are also uninformative:

```{r inform.nancy, message = TRUE}
data(nancycats)
naninform <- informloci(nancycats, cutoff = 0.05)
```

Multilocus Genotype Analysis {#mlg}
============================

In populations with mixed sexual and clonal reproduction, it common to
have multiple samples from the same population that have the same set of
alleles at all loci. Here, we introduce tools for tracking MLGs within
and across populations in `genind`, and `genlight` objects from the
*adegenet* package. We will be using the H3N2 data set
containing SNP data from isolates of the H3N2 virus from 2002 to 2006.
Note that `genclone` and `snpclone` objects are optimal for these analyses. For a more
in-depth document on methods for multilocus genotypes in
*poppr*, see the "Multilocus Genotype Analysis" vignette by
typing

```{r eval = FALSE}
vignette("mlg", package = "poppr")
```

How many multilocus genotypes are in our data set? {Just a peek} {#mlg:mlg}
----------------------------------------------------------------

Counting the number of MLGs in a population is the first step for these analyses
as they allow us to see how many clones exist. With the `genclone` object, This
information is already displayed when we view the object.

```{r view_mlg}
H3N2
```

If we need to store the number of MLGs as a variable, we can simply run
the `mlg()` command.

```{r mlg_genind}
H3N2_mlg <- mlg(H3N2)
H3N2_mlg
```



Since the number of individuals exceeds the number of multilocus
genotypes, we conclude that this data set contains clones. Let"s examine
what populations these clones belong to.

MLGs across populations {clone-ing around} {#mlg:cross}
------------------------------------------

Since you have the ability to define hierarchical levels of your data
set freely, it is quite possible to see some of the same MLGs across
different populations. Tracking them by hand can be a nightmare with
large data sets. Luckily, `mlg.crosspop` has you covered in that regard.

### Function: mlg.crosspop {#mlg:cross:mlg.crosspop}

Analyze the MLGs that cross populations within your data set. This has
three output modes. The default one gives a list of MLGs, and for each
MLG, it gives a named numeric vector indicating the abundance of that
MLG in each population. Alternate outputs are described with
`indexreturn` and `df`.

------------------------------------------------------------------------

```{r mlg.crosspop_command, echo = FALSE, comment = NA, size = 'normalsize'}
funk <- "mlg.crosspop"
print_command(funk)
```

-   `pop -` a `genind` object.

-   `sublist -` Populations to include (Defaults to "ALL"). see `popsub()`

-   `blacklist -` Populations to exclude. see `popsub()`

-   `mlgsub -` see `mlg.table()` Only analyze specified MLGs. The
    vector for this flag can be produced by this function as you will
    see later in this vignette.

-   `indexreturn -` return a vector of indices of MLGs. (You can use
    these in the `mlgsub` flag, or you can use them to subset the
    columns of an MLG table).

-   `df -` return a data frame containing the MLGs, the populations they
    cross, and the number of copies you find in each population. This is
    useful for making graphs in *ggplot2*.

-   `quiet -` `TRUE` or `FALSE`. Should the populations be printed to
    screen as they are processed? (will print nothing if `indexreturn`
    is `TRUE`)

------------------------------------------------------------------------

We can see what MLGs cross different populations and then give a vector
that shows how many populations each one of those MLGs crosses.

```{r crosspop, eval=FALSE}
setPop(H3N2) <- ~country
v.dup <- mlg.crosspop(H3N2, quiet=TRUE)
```

Here is a snippet of what the output looks like when `quiet` is `FALSE`.
It will print out the MLG name, the total number of individuals that
make up that MLG, and the populations where that MLG can be found.

```{r crosspopout, echo=FALSE}
setPop(H3N2) <- ~country
v.dup <- structure(list(MLG.3 = structure(c(4L, 8L), .Names = c("USA",
"Denmark")), MLG.9 = structure(c(1L, 13L, 1L, 1L), .Names = c("Japan",
"USA", "Finland", "Denmark")), MLG.31 = structure(c(2L, 7L), .Names = c("Japan",
"Canada")), MLG.75 = structure(c(2L, 8L, 2L, 1L, 6L, 2L, 1L,
1L), .Names = c("Japan", "USA", "Finland", "Norway", "Denmark",
"Austria", "Russia", "Ireland")), MLG.80 = structure(c(1L, 1L
), .Names = c("USA", "Denmark")), MLG.86 = structure(3:4, .Names = c("Denmark",
"Austria")), MLG.95 = structure(c(1L, 1L), .Names = c("USA",
"Bangladesh")), MLG.97 = structure(c(1L, 5L, 1L, 1L), .Names = c("USA",
"Austria", "Bangladesh", "Romania")), MLG.104 = structure(1:2, .Names = c("USA",
"France")), MLG.110 = structure(c(2L, 3L, 11L), .Names = c("Japan",
"USA", "China"))), .Names = c("MLG.3", "MLG.9", "MLG.31", "MLG.75",
"MLG.80", "MLG.86", "MLG.95", "MLG.97", "MLG.104", "MLG.110"))
printthings <- function(ind, x){
  cat(paste0(names(x)[ind], ":"),
      paste0("(", sum(x[[ind]])," inds)"),
      names(x[[ind]]),
      "\n")
}
invisible(lapply(1:10, printthings, v.dup))
```
The output of this function is a list of MLGs, each containing a vector
indicating the number of copies in each population. We"ll count the
number of populations each MLG crosses using the function `sapply` to
loop over the data with the function `length`.

```{r crosspop2}
head(v.dup)
v.num <- sapply(v.dup, length) # count the number of populations each MLG crosses.
head(v.num)
```

Producing MLG tables and graphs {bringing something to the table} {#mlg:table}
-----------------------------------------------------------------

We can also create a table of MLGs per population as well as bar graphs
to give us a visual representation of the data. This is achieved through
the function `mlg.table`

### Function: mlg.table {#mlg:table:mlg.table}

This function will produce a matrix containing counts of MLGs (columns)
per population (rows). If there are not populations defined in your data
set, a vector will be produced instead.

------------------------------------------------------------------------

```{r mlg.table_command, echo = FALSE, comment = NA, size = 'normalsize'}
funk <- "mlg.table"
print_command(funk)
```
-   `pop -` a `genind` object.

-   `sublist -` Populations to include (Defaults to "ALL"). see `popsub()`

-   `blacklist -` Populations to exclude. see `popsub()`

-   `mlgsub -` a vector containing the indices of MLGs you wish to
    subset your table with.

-   `plot -` `TRUE` or `FALSE`. If `TRUE`, a bar plot will be printed
    for each population with more than one individual.

-   `total -` When set to `TRUE`, the pooled data set will be added to
    the table. Defaults to `FALSE`.

-   `quiet -` Defaults to `FALSE`: population names will be printed to
    the console as they are processed.

------------------------------------------------------------------------

```{r mlgbar, eval = FALSE}
v.tab <- mlg.table(H3N2, plot = TRUE)
v.tab[1:10, 1:10] # Showing the first 10 columns and rows of the table.
```

```{r mlgbarshow, echo = FALSE}
v.tab <- mlg.table(H3N2, plot = FALSE)
v.tab[1:10, 1:10] # Showing the first 10 columns and rows of the table.
```

```{r mlgbarplot, results = 'hide', fig.cap = "An example of a bar-chart produced by `mlg.table`. Note that this data set would produce several such charts but only the chart for Norway is shown here."}
mlg.table(H3N2, sublist = "Norway", plot = TRUE)
```

The MLG table is not limited to use with *poppr*. In fact,
one of the main advantages of `mlg.table()` is that it allows easy access
to diversity functions present in the package *vegan* [@vegan]. One
example is to create a rarefaction curve for each population in your
data set giving the number of expected MLGs for a given sample size. For
more information, type `help(diversity, package=vegan)` in your R
console.

For the sake of this example, instead of drawing a curve for each of the
37 countries represented in this sample, let"s set the hierarchical
level to year.

```{r mlgrare1_dummy, eval = FALSE}
setPop(H3N2) <- ~year
summary(H3N2) # Check the data to make sure it's correct.
```

```{r mlgrare1, echo = FALSE}
setPop(H3N2) <- ~year
res <- "
// Number of individuals: 1903
// Group sizes: 158 415 399 469 462
// Number of alleles per locus: 3 3 4 2 4 2 3 2 4 3 4 2 4 3 2 2 3 3 2 2 3 3 3 2 2 2 2 2 2 2 2 2 2 4 4 3 3 3 4 2 2 2 4 3 2 3 4 2 3 2 3 2 2 2 4 2 2 2 2 2 2 2 4 4 4 3 3 2 3 4 3 2 3 3 3 3 2 3 2 4 2 3 2 2 3 3 3 3 2 2 2 2 3 2 3 2 3 2 3 2 3 3 2 2 2 3 2 2 2 3 3 3 2 2 3 3 3 3 4 2 3 3 4 3 2
// Number of alleles per group: 203 255 232 262 240
// Percentage of missing data: 2.36 %
// Observed heterozygosity: 0"
cat(res, sep = "\n")
```

```{r mlgrare2, eval=FALSE, tidy=FALSE}
library("vegan")
H.year <- mlg.table(H3N2, plot = FALSE)
rarecurve(H.year, ylab="Number of expected MLGs", sample=min(rowSums(H.year)),
          border = NA, fill = NA, font = 2, cex = 1, col = "blue")
```

```{r mlgrareplot, echo=FALSE, results='hide', fig.cap = "An example of a rarefaction curve produced using a MLG table."}
# library("vegan")
H.year <- mlg.table(H3N2, plot = FALSE)
vegan::rarecurve(H.year, ylab = "Number of expected MLGs", col = "blue",
    sample = min(rowSums(H.year)), border = NA, fill = NA, font = 2, cex = 1)
```

The minimum value from the *base* function `rowSums()` of the table
represents the minimum common sample size of all populations defined in
the table. Setting the "sample" flag draws the horizontal and vertical
lines you see on the graph. The intersections of these lines correspond
to the numbers you would find if you ran the function `poppr` on this
data set (under the column "`eMLG`").

Combining MLG functions {getting into the mix} {#mlg:mix}
----------------------------------------------

Alone, the different functionalities are neat. Combined, we can create
interesting data sets. Let"s say we wanted to know which MLGs were
duplicated across the regions of the United Kingdom, Germany,
Netherlands, and Norway. All we have to do is use the `sublist` flag in
the function:

```{r subcross}
setPop(H3N2) <- ~country
UGNN.list <- c("United Kingdom", "Germany", "Netherlands", "Norway")
UGNN <- mlg.crosspop(H3N2, sublist=UGNN.list, indexreturn=TRUE)
```

OK, the output tells us that there are three MLGs that are crossing
between these populations, but we do not know how many are in each. We
can easily find that out if we subset our original table, `v.tab`.


```{r subtable}
UGNN # Note that we have three numbers here. This will index the columns for us.
UGNN.list # And let's not forget that we have the population names.
v.tab[UGNN.list, UGNN]
```

Now we can see that Norway has a higher incidence of nearly all of these
MLGs. We can investigate the incidence of these MLGs throughout our data
set. One thing that the `genclone` object keeps track of is
a single vector defining the unique multilocus genotypes within the
data. These are represented as integers and can be accessed with
`mlg.vector`. This is useful for finding MLGs that correspond to certain
individuals or populations. Let"s use `mlg.vector` to find individuals
corresponding to the MLGs. First we"ll investigate what the output of
this function looks like.

```{r mlg.vector_first}
v.vec <- mlg.vector(H3N2)
str(v.vec) # Analyze the structure.
```

The integers produced are the MLG assignment of each individual in the
same order as the data set. This means that the first two individuals
have the exact same set of alleles at each locus, so they have the same
MLG: 605. If we look at the number of unique integers in the vector, it
corresponds to the number of observed multilocus genotypes:

```{r mlg.vector_second}
length(unique(v.vec)) # count the number of MLGs
H3N2 # equal to the first number in this output.
```

 We will take `UGNN` (MLGs crossing UK, Germany,
Netherlands, and Norway) and compare its elements to the MLG vector
(`v.vec`) to see where else they occur.

```{r mlg.vector_match}
UGNN # Show what we are looking for
UGNN_match <- v.vec %in% UGNN
table(UGNN_match) # How many individuals matched to those three MLGs?
```


22 individuals matched to those three MLGs. We can use this vector to
show us the 22 individuals.

```{r mlg.vector_inds}
indNames(H3N2)[UGNN_match]
```

Note that there is an alternative way to list individuals matching
specific MLGs using the function `mlg.id`. This function will return a
list where each element represents a unique MLG. You can use this data
to find out which individuals correspond to specific MLGs. Each element
in the list is named with the MLG, but the index does not necessarily
match up, so it is important to convert your query MLGs to strings:


```{r mlg.id}
H3N2.id <- mlg.id(H3N2)
H3N2.id[as.character(UGNN)]
```

We can also use the vector of MLGs to subset `mlg.table()` with the
`mlgsub` flag.

```{r mlgsub_flag, eval=FALSE}
mlg.table(H3N2, mlgsub = UGNN)
```

That showed us exactly which populations these three MLGs came from in
our data set.

```{r mlgsub_flagshow, results='hide', echo=FALSE, fig.cap = "All populations that contain these MLGs"}
mlg.table(H3N2, mlgsub = UGNN)
```

Appendix
========

Manipulating Graphics
---------------------

*Poppr* utilizes *ggplot2* to produce many of its graphs.
One advantage it gives the user is the ability to manipulate these
graphs. With base R graphs, the only manipulation that can be performed
is by adding elements to the graph. It is a static image. The ggplot
graphs are actually represented as objects in your R environment. We can
use the function `last_plot()` from *ggplot2* to be able to grab the
plot that was plotted last in our window. Let's illustrate this using a
MLG bar graph from the Aeut data set.

```{r Aeut_MLG_Aeut, fig.width = 7, fig.height = 3}
library("poppr")
library("ggplot2")
data(Aeut)
Aeut.tab <- mlg.table(Aeut)
p <- last_plot()
```

We've captured our plot using `last_plot()` and now we can manipulate
it. One common need is to change the title. We can easily do that with
the function `ggtitle()`. Let"s say we wanted to label it "*Aphanomyces
euteiches* multilocus genotype distribution". We would use
`ggtitle(Aphanomyces euteiches multilocus genotype distribution)`.
Unfortunately, we need italics for a latin binomial. One way to acheive
this is by using the `expression()` function and declaring which text
needs to be italicized.

```{r Aeut_MLG_title, fig.width = 7, fig.height = 3}
myTitle <- expression(paste(italic("Aphanomyces euteiches"), 
                      " multilocus genotype distribution"))
(pt <- p +
   ggtitle(myTitle) +
   xlab("Multilocus genotype")) # We can label the x axis, too
```

Let"s say we wanted to remove the grey background. We could use the
`theme()` function to do this, or we could use a theme already
implemented in ggplot2 called `theme_bw()`.

```{r Aeut_MLG_theme, fig.width = 7, fig.height = 3}
(ptt <- pt + theme_bw())
```

Uh-oh. The x axis labels are now horizontal when they should be
vertical. Since it"s the overall distribution we're interested in, we
don't really need them anyways. We can remove them with `axis.text.x`
and `axis.ticks.x` (we'll also remove the x axis gridlines because they're ugly).

```{r Aeut_MLG_theme_axis, fig.width = 7, fig.height = 3}
(ptta <- ptt + theme(axis.text.x = element_blank()) +
  theme(axis.ticks.x = element_blank()) +
  theme(panel.grid.major.x = element_blank()))
```

And, if for some bizarre reason, you liked the color gradient in
*poppr* version 1, you can get that back by adding the
*fill* aesthetic:

```{r Aeut_silly, fig.width = 7, fig.height = 3}
(ptttaf <- ptta + aes(fill = count))
```

This allows you to produce publication quality graphs directly in R.
Please see Hadley Wickham"s *ggplot2* package for more details
[@ggplot2]. Note that if you don"t like using *ggplot2*, you can access
the data in the ggplot2 object and plot the data yourself:

```{r Aeut_MLG_data}
head(p$data)
```


Exporting Graphics {#appendix:graphics}
------------------

R has the ability to produce nice graphics from most any type of data,
but to get these graphics into a report, presentation, or manuscript can
be a bit challenging. It"s no secret that the R Documentation pages are
a little difficult to interpret, so I will give the reader here a short
example on how to export graphics from R. Note that any code here that
will produce images will also be present in other places in this
vignette. The default installation of the R GUI is quite minimal, and
for an easy way to manage your plots and code, I strongly encourage the
user to use Rstudio <http://www.rstudio.com/>.

### Basics {#appendix:graphics:basics}

Before you export graphics, you have to ask yourself what they will be
used for. If you want to use the graphic for a website, you might want
to opt for a low-resolution image so that it can load quickly. With
printing, you"ll want to make sure that you have a scalable or at least
a very high resolution image. Here, I will give some general guidelines
for graphics (note that these are merely suggestions, not defined
rules).

-   **What you see is not always what you get** I have often seen
    presentations where the colors were too light or posters with
    painfully pixellated graphs. Think about what you are going to be
    using a graphic for and how it will appear to the intended audience
    given the media type.

-   **$\geq$ 300 dpi unless its for a web page** For any sort of printed
    material that requires a raster based image, 300dpi (dots per inch)
    is the absolute minimum resolution you should use. For simple black
    and white line images, 1200dpi is better. This will leave you with
    crisp, professional looking images.

-   **If possible, save to SVG, then rasterize** Raster images (bmp,
    png, jpg, etc...) are based off of the number of pixels or dots per
    inch it takes to render the image. This means that the raster image
    is more or less a very fine mosaic. Vector images (SVG) are built
    upon several interconnected polygons, arcs, and lines that scale
    relative to one another to create your graphic. With vector
    graphics, you can produce a plot and scale it to the size of a
    building if you wanted to. When you save to an SVG file first, you
    can also manipulate it in programs such as Adobe Illustrator
    or Inkscape.

-   **Before saving, make sure the units and dimensions are correct**
    Unless you really wanted to save a graph that"s over 6 feet wide.

### Image Editors {#appendix:graphics:editors}

Often times, fine details such as labels on networks need to be tweaked
by hand. Luckily, there are a wide variety of programs that can help you
do that. Here is a short list of image editors (both free and for a
price) that you can use to edit your graphics.

-   Bitmap based editors (for jpeg, bmp, png, etc...)

    -   **The GIMP**
        Free, cross-platform. <http://www.gimp.org>
    -   **Paint.net** Free,
        Windows only. <http://www.getpaint.net>
    -   **Adobe Photoshop**
        Pricey, Windows and Mac.
        <http://www.adobe.com/products/photoshop.html>
-   Scalable Vector Graphics based editors (for svg, pdf)
    -   **Inkscape**  Free, cross-platform <http://inkscape.org>
    -   **Adobe Illustrator** Pricey, Windows and Mac.
        <http://www.adobe.com/products/illustrator.html>
    
### Exporting ggplot2 graphics {#appendix:graphics:ggplot2}

*ggplot2* is a fantastic package that *poppr* uses to
produce graphs for the `mlg.table()`, `poppr()`, and `ia()` functions. Saving
a plot with *ggplot2* is performed with one command after your plot has
rendered:

```{r ggsave1, eval=FALSE}
data(nancycats) # Load the data set.
poppr(nancycats, sample = 999) # Produce a single plot.
ggsave("nancycats.pdf")
```

Note that you can name the file anything, and `ggsave` will save it in
that format for you. The details are in the documentation and you can
access it by typing `help(ggsave)` in your R console. The important
things to note are that you can set a `width`, `height`, and `unit`. The
only downside to this function is that you can only save one plot at a
time. If you want to be able to save multiple plots, read on to the next
section.

### Exporting any graphics {#appendix:graphics:export}

Some of the functions that *poppr* offers will give you
multiple plots, and if you want to save them all, using `ggsave` will
require a lot of tedious typing and clicking. Luckily, R has Functions
that will save any plot you generate in nearly any image format you
want. You can save in raster images such as png, bpm, and jpeg. You can
also save in vector based images such as svg, pdf, and postscript. The
important thing to remember is that when you are saving in a raster
format, the default units of measurement are "pixels", but you can
change that by specifying your unit of choice and a resolution.

For raster images and svg files, you can only save your plots in
multiple files, but pdf and postscript plots can be saved in one file as
multiple pages. All of these functions have the same basic form. You
call the function to specify the file type you want (eg.
`pdf(myfile.pdf)`), create any graphs that you want to create, and then
make sure to close the session with the function `dev.off()`. Let"s give
an example saving to pdf and png files.

```{r png_save, eval=FALSE}
data(nancycats)

png("nancy_pair%02d.png", width = 14, height = 14, units = "in", res = 300)
poppairs <- lapply(seppop(nancycats), pair.ia, limits = c(-0.25, 1))
dev.off()

```

Since this data set is made up of 30 populations with more than 1
individual, this will save 30 files to your working directory named
"nancy\_pair\_barchart01.png...nancy\_pair\_barchart30.png". The way R
knows how to number these files is because of the `%02d` part of the
command. That"s telling R to use a number that is two digits long in
place of that expression. All of these files will be 14x14" and will
have a resolution of 300 dots per inch. If you wanted to do the same
thing, but place them all in one file, you should use the pdf option.

```{r pdf_save, eval=FALSE}
pdf("nancy_pair.png", width = 14, height = 14, compress = FALSE)
poppairs <- lapply(seppop(nancycats), pair.ia, limits = c(-0.25, 1))
dev.off()
```

Remember, it is important not to forget to type `dev.off()` when you are
done making graphs. Note that I did not have to specify a resolution for
this image since it is based off of vector graphics.

[^1]: This window sometimes appears behind your current session of R,
    depending on the GUI and you will have to toggle to this window

[^2]: \*.CSV files are comma separated files that are easily machine
    readable. 

[githubsite]: https://github.com/grunwaldlab/Population_Genetics_in_R

References
==========
